SoroushVahidi/scidocs huggingface dataset:
Cooperation of the basal ganglia, cerebellum, sensory cerebrum and hippocampus: possible implications for cognition, consciousness, intelligence and creativity
(2001)

It is suggested that the anatomical structures which mediate consciousness evolved as decisive embellishments to a (non-conscious) design strategy present even in the simplest unicellular organisms. Consciousness is thus not the pinnacle of a hierarchy whose base is the primitive reflex, because reflexes require a nervous system, which the single-celled creature does not possess. By postulating that consciousness is intimately connected to self-paced probing of the environment, also prominent in prokaryotic behavior, one can make mammalian neuroanatomy amenable to dramatically straightforward rationalization. Muscular contraction is the nervous system's only externally directed product, and the signaling routes which pass through the various brain components must ultimately converge on the motor areas. The function of several components is still debatable, so it might seem premature to analyze the global operation of the circuit these routes constitute. But such analysis produces a remarkably simple picture, and it sheds new light on the roles of the individual components. The underlying principle is conditionally permitted movement, some components being able to veto muscular contraction by denying the motor areas sufficient activation. This is true of the basal ganglia (BG) and the cerebellum (Cb), which act in tandem with the sensory cerebrum, and which can prevent the latter's signals to the motor areas from exceeding the threshold for overt movement. It is also true of the anterior cingulate, which appears to play a major role in directing attention. In mammals, the result can be mere thought, provided that a second lower threshold is exceeded. The veto functions of the BG and the Cb stem from inhibition, but the countermanding disinhibition develops at markedly different rates in those two key components. It develops rapidly in the BG, control being exercised by the amygdala, which itself is governed by various other brain regions. It develops over time in the Cb, thereby permitting previously executed movements that have proved advantageous. If cognition is linked to overt or covert movement, intelligence becomes the ability to consolidate individual motor elements into more complex patterns, and creativity is the outcome of a race-to-threshold process which centers on the motor areas. Amongst the ramifications of these ideas are aspects of cortical oscillations, phantom limb sensations, amyotrophic lateral sclerosis (ALS) the difficulty of self-tickling and mirror neurons.
Incentive mechanism for peer-to-peer media streaming
(2004)

We propose a rank-based peer-selection mechanism for peer-to-peer media streaming systems. The mechanism provides incentives for cooperation through service differentiation. Contributors to the system are rewarded with flexibility and choice in peer selection, resulting in high quality streaming sessions. Free-riders are given limited options in peer selection, if any, and hence receive low quality streaming. Through simulation and wide-area measurement studies, we verify that the mechanism can provide near optimal streaming quality to the cooperative users until the bottleneck shifts from the sources to the network.
Model-based Q-learning for humanoid robots
(2017)

This paper is proposal with regarding reinforcement learning and robotics. It contains our study so far about reinforcement learning problem and Q-learning — one of the methods to solve it. The method is tested both by running on a simulation and on NAO robot. Both are written in high programming language. Since the testing are also done on NAO robot. This paper also includes our understanding about NAO robot and Robotic Operating System (ROS), and our approach to apply Q-learning on NAO robot. In the end, we have been successfully tested Q-learning method and apply it to NAO robot in real-world environment.
Community detection and visualization in social networks: Integrating structural and semantic information
(2013)

Due to the explosion of social networking and the information sharing among their users, the interest in analyzing social networks has increased over the recent years. Two general interests in this kind of studies are community detection and visualization. In the first case, most of the classic algorithms for community detection use only the structural information to identify groups, that is, how clusters are formed according to the topology of the relationships. However, these methods do not take into account any semantic information which could guide the clustering process, and which may add elements to conduct further analyses. In the second case most of the layout algorithms for clustered graphs have been designed to differentiate the groups within the graph, but they are not designed to analyze the interactions between such groups. Identifying these interactions gives an insight into the way different communities exchange messages or information, and allows the social network researcher to identify key actors, roles, and paths from one community to another.
 This article presents a novel model to use, in a conjoint way, the semantic information from the social network and its structural information to, first, find structurally and semantically related groups of nodes, and second, a layout algorithm for clustered graphs which divides the nodes into two types, one for nodes with edges connecting other communities and another with nodes connecting nodes only within their own community. With this division the visualization tool focuses on the connections between groups facilitating deep studies of augmented social networks.
C4: the continuously concurrent compacting collector
(2011)

C4, the Continuously Concurrent Compacting Collector, an updated generational form of the Pauseless GC Algorithm [7], is introduced and described, along with details of its implementation on modern X86 hardware. It uses a read barrier to support concur- rent compaction, concurrent remapping, and concurrent incremental update tracing. C4 differentiates itself from other generational garbage collectors by supporting simultaneous-generational concurrency: the different generations are collected using concurrent (non stop-the-world) mechanisms that can be simultaneously and independently active. C4 is able to continuously perform concurrent young generation collections, even during long periods of concurrent full heap collection, allowing C4 to sustain high allocation rates and maintain the efficiency typical to generational collectors, without sacrificing response times or reverting to stop-the-world operation. Azul systems has been shipping a commercial implementation of the Pauseless GC mechanism, since 2005. Three successive generations of Azul's Vega series systems relied on custom multi-core processors and a custom OS kernel to deliver both the scale and features needed to support Pauseless GC. In 2010, Azul released its first software-only commercial implementation of C4 for modern commodity X86 hardware, using Linux kernel enhancements to support the required feature set. We discuss implementa- tion details of C4 on X86, including the Linux virtual and physical memory management enhancements that were used to support the high rate of virtual memory operations required for sustained pauseless operation. We discuss updates to the collector's manage- ment of the heap for efficient generational collection and provide throughput and pause time data while running sustained workloads.
A study of normative and informational social influences upon individual judgement.
(1955)

By NOW, many experimental studies (e.g., 1, 3, 6) have demonstrated that individual psychological processes are subject to social influences. Most investigators, however, have not distinguished among different kinds of social influences; rather, they have carelessly used the term "group" influence to characterize the impact of many different kinds of social factors. In fact, a review of the major experiments in this area—e.g., those by Sherif (6), Asch (1), Bovard (3)—would indicate that the subjects (5s) in these experiments as they made their judgments were not functioning as members of a group in any simple or obvious manner. The S, in the usual experiment in this area, made perceptual judgments hi the physical presence of others after hearing their judgments. Typically, the S was not given experimental instructions which made him feel that he was a member of a group faced with a common task requiring cooperative effort for its most effective solution. If "group" influences were at work in the foregoing experiments, they were subtly and indirectly created rather than purposefully created by the experimenter.
T REE-STRUCTURED DECODING WITH DOUBLY-RECURRENT NEURAL NETWORKS
(2017)

We propose a neural network architecture for generating tree-structured objects from encoded representations. The core of the method is a doubly recurrent neural network model comprised of separate width and depth recurrences that are combined inside each cell (node) to generate an output. The topology of the tree is modeled explicitly together with the content. That is, in response to an encoded vector representation, co-evolving recurrences are used to realize the associated tree and the labels for the nodes in the tree. We test this architecture in an encoderdecoder framework, where we train a network to encode a sentence as a vector, and then generate a tree structure from it. The experimental results show the effectiveness of this architecture at recovering latent tree structure in sequences and at mapping sentences to simple functional programs.
Extreme Learning Machine for Multilayer Perceptron
(2016)

Extreme learning machine (ELM) is an emerging learning algorithm for the generalized single hidden layer feedforward neural networks, of which the hidden node parameters are randomly generated and the output weights are analytically computed. However, due to its shallow architecture, feature learning using ELM may not be effective for natural signals (e.g., images/videos), even with a large number of hidden nodes. To address this issue, in this paper, a new ELM-based hierarchical learning framework is proposed for multilayer perceptron. The proposed architecture is divided into two main components: 1) self-taught feature extraction followed by supervised feature classification and 2) they are bridged by random initialized hidden weights. The novelties of this paper are as follows: 1) unsupervised multilayer encoding is conducted for feature extraction, and an ELM-based sparse autoencoder is developed via ℓ1 constraint. By doing so, it achieves more compact and meaningful feature representations than the original ELM; 2) by exploiting the advantages of ELM random feature mapping, the hierarchically encoded outputs are randomly projected before final decision making, which leads to a better generalization with faster learning speed; and 3) unlike the greedy layerwise training of deep learning (DL), the hidden layers of the proposed framework are trained in a forward manner. Once the previous layer is established, the weights of the current layer are fixed without fine-tuning. Therefore, it has much better learning efficiency than the DL. Extensive experiments on various widely used classification data sets show that the proposed algorithm achieves better and faster convergence than the existing state-of-the-art hierarchical learning methods. Furthermore, multiple applications in computer vision further confirm the generality and capability of the proposed learning scheme.
Motivational Leadership: Tips From the Business World.
(2016)

It is an important task for leadership to identify the motivating factors for employees and motivate them to fulfill their individual and organizational goals. Although there are several motivational factors (extrinsic and intrinsic), intrinsic motivational factors such as autonomy, mastery, and purpose are more important for deeper lasting job satisfaction and higher performance. In this article, the authors discuss how an understanding of these factors that influence motivation has the potential to transform an organization.
Stronger Baselines for Trustable Results in Neural Machine Translation
(2017)

Interest in neural machine translation has grown rapidly as its effectiveness has been demonstrated across language and data scenarios. New research regularly introduces architectural and algorithmic improvements that lead to significant gains over “vanilla” NMT implementations. However, these new techniques are rarely evaluated in the context of previously published techniques, specifically those that are widely used in state-of-theart production and shared-task systems. As a result, it is often difficult to determine whether improvements from research will carry over to systems deployed for real-world use. In this work, we recommend three specific methods that are relatively easy to implement and result in much stronger experimental systems. Beyond reporting significantly higher BLEU scores, we conduct an in-depth analysis of where improvements originate and what inherent weaknesses of basic NMT models are being addressed. We then compare the relative gains afforded by several other techniques proposed in the literature when starting with vanilla systems versus our stronger baselines, showing that experimental conclusions may change depending on the baseline chosen. This indicates that choosing a strong baseline is crucial for reporting reliable experimental results.
TMCoI-SIOT: A trust management system based on communities of interest for the social Internet of Things
(2017)

We propose a trust management system (TMS) for the social Internet of things (SIOT) called TMCoI-SIOT. The proposed system integrates numerous factors such as direct and indirect trust; transaction factors and social modeling of trust. The novelty of our approach can be summed up in three aspects. The first aspect concerns the network architecture which is seen as a set of nodes groups called community of interest (CoI). Each community nodes shares the same interests and is headed by an administrator (admin. The second aspect is the trust management system that prevents On-Off attacks. The third aspect concerns the trust prediction method that uses the Kalman filter technique in order to estimate nodes behaviors and prevent possible attacks. We prove the effectiveness of the proposed system by simulation against TMS attacks.
Two comparison-alternative high temperature PCB-embedded transformer designs for a 2 W gate driver power supply
(2016)

With fast power semiconductor devices based on GaN and SiC becoming more common, there is a need for improved driving circuits. Transformers with smaller inter-winding capacitance in the isolated gate drive power supply helps in reducing the conducted EMI emission from the power converter to auxiliary sources. This paper presents a transformer with a small volume, a low power loss and a small inter-capacitance in a gate drive power supply to fast switching devices, such as GaN HEMT and SiC MOSFET. The transformer core is embedded into PCB to increase the integration density. Two different transformer designs, the coplanar-winding PCB embedded transformer and the toroidal PCB embedded transformer, are presented and compared. The former has a 0.8 pF inter-capacitance and the latter has 85% efficiency with 73 W/in3 power density. Both designs are dedicated to a 2 W gate drive power supply for wide-band-gap device, which can operate at 200 °C ambient temperature.
Towards Effective Log Summarization
(2016)

Database access logs are the canonical go-to resource for tasks ranging from performance tuning to security auditing. Unfortunately, they are also large, unwieldy, and it can be difficult for a human analyst to divine the intent behind typical queries in the log. With an eye towards creating tools for ad-hoc exploration of queries by intent, we analyze techniques for clustering queries by intent. Although numerous techniques have already been developed for log summarization, they target specific goals like query recommendation or storage layout optimization rather than the more fuzzy notion of query intent. In this paper, we first survey a variety of log summarization techniques, focusing on a class of approaches that use query similarity metrics. We then propose DCABench, a benchmark that evaluates how well query similarity metrics capture query intent, and use it to evaluate three similarity metrics. DCABench uses student answers to query construction assignments to capture a wide range of distinct SQL queries that all have the same intent. Next, we propose and evaluate a query regularization process that standardizes query representations, significantly improving the effectiveness of the three similarity metrics tested. Finally, we propose an entirely new similarity metric based on the Weisfeiler-Lehman (WL) approximate graph isomorphism algorithm, which identifies salient features of a graph — or in our case, of the abstract syntax tree of a query. We show experimentally that distances in WL-feature space capture a meaningful notion of similarity, while still retaining competitive performance.
Guided-wave and leakage characteristics of substrate integrated waveguide
(2005)

The substrate integrated waveguide (SIW) technique makes it possible that a complete circuit including planar circuitry, transitions, and rectangular waveguides are fabricated in planar form using a standard printed circuit board or other planar processing techniques. In this paper, guided wave and modes characteristics of such an SIW periodic structure are studied in detail for the first time. A numerical multimode calibration procedure is proposed and developed with a commercial software package on the basis of a full-wave finite-element method for the accurate extraction of complex propagation constants of the SIW structure. Two different lengths of the SIW are numerically simulated under multimode excitation. By means of our proposed technique, the complex propagation constant of each SIW mode can accurately be extracted and the electromagnetic bandstop phenomena of periodic structures are also investigated. Experiments are made to validate our proposed technique. Simple design rules are provided and discussed.
Low-loss ultra-wideband transition between conductor-backed coplanar waveguide and substrate integrated waveguide
(2009)

A novel transition between conductor-backed coplanar waveguide (CBCPW) and substrate integrated waveguide (SIW) is presented for microwave and millimeter-wave integrated circuit design. The proposed integrated transition that can provide simultaneous field and impedance matching, exhibits outstanding low-loss performances over an ultra-wideband range (entire Ka-band in our case). In this work, a generalized impedance inverter whose parameters are accurately extracted by the use of a numerical Thru-Reflection-Line (TRL) calibration technique is utilized to design the transition. Measured results for the fabricated transition in back-to-back configuration show that the insertion loss is better than 0.4 dB while the return loss is better than 20 dB over the entire Ka-band.
A pseudo-class-AB telescopic-cascode operational amplifier
(2004)

A class-AB architecture for single-stage operational amplifiers is presented. The structure employs a switched-capacitor level shifter to apply the signal to both sink and source output transistors to create the class-AB behavior. Using this structure, the current mirror circuit of traditional class-AB structures can be eliminated. Thus some power can be saved and the operation frequency can be increased. Simulation results of a fast-settling telescopic-cascode op-amp employing this pseudo-class-AB technique confirm the effectiveness of the approach to reduce the power consumption. This approach can be applied to the first stage of a two-stage op-amp as well, to increase the slew rate. The simulated 12-bit 100-Ms/s sample-and-hold amplifier employing the proposed pseudo-class-AB telescopic-cascode op-amp consumes only 16 mW from 3.3 V power supply.
Flux-Weakening Control of Electric Starter–Generator Based on Permanent-Magnet Machine
(2017)

This paper presents the control analysis and design for a permanent-magnet machine (PMM) operated in the flux-weakening (FW) mode for an aircraft electric starter–generator (SG) application. Previous literature has focused on FW control of PMMs in the motoring (starting) mode; however, the system stability and control in the generating mode have been inadequately studied. This paper reports detailed, rigorous control analysis and design for a PMM-based aircraft electric SG operated in the FW mode. It is shown that an unstable area of operation exists. A novel control scheme which eliminates this instability is proposed. The key analytical findings of the paper are verified by experimental investigation. This paper therefore concludes that the presented technique is able to ensure system stability under all modes of operation. Furthermore, it is noted that the findings of this work are also valuable for any two-quadrant PMM drive with frequent change between starting and generating regimes under current-limiting operation.
Implicit Subjective and Sentimental Usages in Multi-sense Word Embeddings
(2018)

In multi-sense word embeddings, contextual variations in corpus may cause a univocal word to be embedded into different sense vectors. Shi et al. (2016) show that this kind of pseudo multi-senses can be eliminated by linear transformations. In this paper, we show that pseudo multi-senses may come from a uniform and meaningful phenomenon such as subjective and sentimental usage, though they are seemingly redundant. In this paper, we present an unsupervised algorithm to find a linear transformation which can minimize the transformed distance of a group of sense pairs. The major shrinking direction of this transformation is found to be related with subjective shift. Therefore, we can not only eliminate pseudo multi-senses in multisense embeddings, but also identify these subjective senses and tag the subjective and sentimental usage of words in the corpus automatically.